The utility of universal urinary drug screening in chronic pain management
Bibliographic record
Abstract
BACKGROUND: A recent systematic review found few studies that assessed the value of urinary drug screening (UDS) in the management of chronic pain. The Pain Management Unit in Halifax, Nova Scotia, has recently implemented tandem mass spectrometry (TMS) UDS for all new patients. AIMS: To study the prevalence of unexpected TMS UDS results at a hospital-based chronic pain center, to assess which drugs are most likely to contribute to an unexpected result and to assess the clinical utilization of unexpected results by pain physicians. METHODS: From June 2014 to June 2016, a total of 664 patients with chronic non-cancer pain (CNCP) were seen for initial consult. Charts were reviewed and used to create a database containing sex, age, UDS result, physician, and medication/illicit drug history. For all unexpected UDS results, an interview was conducted with the treating physician to determine its clinical implications. RESULTS: For the general pain specialists, the overall percentage of patients with an unexpected UDS result was 16.67%. Excluding codeine, at most 4.47% of patients tested unexpectedly positive for a strong opioid. Although eight out of nine physicians found UDS helpful in general, only 29.58% of unexpected results were helpful in the management of their patients and directly influenced their care. CONCLUSIONS: The prevalence of an unexpected UDS result in patients with CNCP is significant. Most physicians agree that UDS is helpful but in only a limited number of cases did the unexpected result provide helpful information that significantly influenced patient care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.116 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".